Zhiping Ying

Rapid in-situ quantitative evaluation of subsurface cracks in carbon fiber-reinforced polymer (CFRP) structures remains challenging. This study proposes an end-to-end method that exploits the anisotropy-dependent eddy-current response of CFRP by combining eddy-current testing (ECT), entropy-difference-based multimodal fusion, and a one-dimensional convolutional neural network. Amplitude, phase, real, and imaginary components were acquired at two excitation frequencies and fused using self-optimizing weights derived from their entropy differences relative to an intact reference. The fused one-dimensional sequence was then used to regress crack center position, width, and subsurface depth simultaneously. On the held-out test set, the model achieved mean absolute errors (MAEs) of 0.44 mm for center position, 0.17 mm for width, and 0.02 mm for depth, with R ² values of at least 0.95. Tests on lightning-shaped crack geometries not represented in the training set further demonstrated geometric transferability within the tested machined-crack domain. These results show that the proposed method effectively fuses complementary information from the four impedance components without direct concatenation, enabling accurate quantitative evaluation of subsurface cracks in CFRP from raw ECT signals.

This paper presents a self-tuned two-stage framework for point cloud reconstruction. A parameter-free denoising module (TPDn) automatically selects thresholds through polynomial model fitting to remove noise and outliers without manual tuning. The denoised cloud is then upsampled by PU-Net to recover fine-grained geometry. This synergy enhances structural consistency and demonstrates qualitative robustness under various noise conditions. Experiments on synthetic datasets and real industrial scans show that the proposed method improves geometric accuracy and uniformity while maintaining low computational cost. The framework is simple, efficient, and easily scalable to large-scale point clouds.

Identifying different features and phases within the biaxial warp-knitted composites enables accurate characterization of the internal changes of the composites under complex loading conditions and different processing parameters. However, obtaining 3D reconstructions that can be accurately segmented and quantitatively analyzed is still a challenge, primarily due to the low attenuation of materials, especially the low contrast between the weft yarn and warp yarns. Here, we use deep learning approaches to identify the boundary of the section of the tow on the 2D image, extract individual warp or weft tows automatically, and this is followed by computational modeling of composite materials. The results show that the performance of ResUNet++ model is better than that of other models for the biaxial warp-knitted composite specimen segmentation tasks. The special improved dataset augmentation algorithm is also a positive effective way to enhance network performance.

Thermography is widely used to detect delamination defects in carbon fiber reinforced plastics (CFRP). This paper proposes a model to detect defects automatically by extracting the thermal signal characteristics of CFRP materials. An optically excited thermography system is constructed for pulsed and lock-in thermography experiments to compare thermal signal data sets in different excitation modes. A multi-task joint loss function is defined to train the model for defect detection and depth prediction. The effects of different attention modules (AM) are analyzed to improve the model performance. By comparing the effects of traditional thermography processing methods and methods based on Convolutional Neural Network (CNN), it is found that the proposed model can detect defects with minimum aspect ratio (ratio of short side to depth) of 2.5, and the relative error percentage in depth prediction is below 10%.